Simulating a local emissions levy to fund local energy effiency retrofit
Focus on: Southampton
Ben Anderson
Last run at: 2021-10-20 14:43:13
1 About
Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
2 Citation
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom
3 Introduction
Background blurb about emissions, retofit, carbon tax/levy etc
4 Emissions Levy Case Study
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy value.
NB: no maps in the interests of speed
4.1 Data
We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
- CREDS place-based emissions estimates (contains data from a wide range of sources including https://epc.opendatacommunities.org/ for EPCs)
- 2018 residential gas & electricity use data
- Indices of Deprivation 2019
- Urban/Rural classification at LSOA level
- various LSOA look-up tables from geoportal
All analysis is at LSOA level. Cautions on inference from area level data apply.
4.2 CREDS place-based emissions estimates
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
- Emissions are presented as per capita…
- Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
## region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: South East 148 7334.257 2838.428Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Southampton 005F Swaythling 881 976 719
## 2: Southampton 020D Freemantle 869 1139 881
## 3: Southampton 026A Sholing 846 908 425
## 4: Southampton 021A Freemantle 750 914 555
## 5: Southampton 030C Sholing 724 769 393
## 6: Southampton 019D Millbrook 718 742 356## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Southampton 031D Woolston 560 1392 1140
## 2: Southampton 029C Bargate 414 1379 1100
## 3: Southampton 023D Bargate 342 1341 1080
## 4: Southampton 029G Bargate 407 1258 1130
## 5: Southampton 020D Freemantle 869 1139 881
## 6: Southampton 029A Bargate 586 1105 794## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Southampton 005F Swaythling 881 976 719
## 2: Southampton 020D Freemantle 869 1139 881
## 3: Southampton 026A Sholing 846 908 425
## 4: Southampton 021A Freemantle 750 914 555
## 5: Southampton 030C Sholing 724 769 393
## 6: Southampton 019D Millbrook 718 742 356Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
4.2.1 Estimate per dwelling emissions
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values| Name | …[] |
| Number of rows | 148 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 17126.66 | 7014.73 | 5845.64 | 11905.74 | 15464.94 | 21050.67 | 44958.42 | ▆▇▃▁▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 1758.06 | 626.82 | 12.82 | 1358.01 | 1727.03 | 2166.35 | 3659.41 | ▁▅▇▃▁ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1004.41 | 109.16 | 655.50 | 938.64 | 988.98 | 1044.12 | 1459.95 | ▁▆▇▂▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 2762.47 | 641.02 | 1123.58 | 2322.68 | 2744.44 | 3169.45 | 4876.37 | ▁▆▇▂▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 117.63 | 166.94 | 0.00 | 40.87 | 64.42 | 112.06 | 1151.73 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 2880.09 | 584.12 | 1506.15 | 2476.45 | 2813.93 | 3235.29 | 5031.52 | ▂▇▅▂▁ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 1848.64 | 566.47 | 613.65 | 1413.59 | 1851.17 | 2258.37 | 3546.98 | ▃▆▇▃▁ |
| CREDSvan_kgco2e2018_pdw | 0 | 1 | 266.07 | 335.98 | 22.61 | 130.24 | 194.83 | 265.58 | 2801.60 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 0 | 1 | 2114.71 | 684.48 | 760.81 | 1607.59 | 2131.50 | 2558.57 | 4223.23 | ▅▇▇▂▁ |
Examine patterns of per dwelling emissions for sense.
4.2.1.1 All emissions
Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -9.9011, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7213850 -0.5262861
## sample estimates:
## cor
## -0.6338111## LSOA11CD WD18NM All_Tco2e_per_dw
## Length:148 Length:148 Min. : 5.846
## Class :character Class :character 1st Qu.:11.906
## Mode :character Mode :character Median :15.465
## Mean :17.127
## 3rd Qu.:21.051
## Max. :44.958## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01017249 Shirley 44.95842
## 2: E01017148 Bassett 43.54419
## 3: E01017197 Freemantle 41.42910
## 4: E01017224 Peartree 31.22609
## 5: E01017180 Coxford 30.70376
## 6: E01017214 Millbrook 30.16370## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01017245 Redbridge 7.967564
## 2: E01017241 Redbridge 7.871967
## 3: E01032738 Bevois 7.870684
## 4: E01017182 Coxford 7.344557
## 5: E01017139 Bargate 7.015385
## 6: E01017140 Bargate 5.8456384.2.1.2 Home energy use
Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12.82 1358.01 1727.03 1758.06 2166.35 3659.41## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -7.7513, df = 146, p-value = 1.421e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6450947 -0.4147371
## sample estimates:
## cor
## -0.53995Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.1523, df = 146, p-value = 0.03301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32744768 -0.01443342
## sample estimates:
## cor
## -0.1753689Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.1523, df = 146, p-value = 0.03301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32744768 -0.01443342
## sample estimates:
## cor
## -0.1753689## RUC11 mean_gas_kgco2e mean_elec_kgco2e mean_other_energy_kgco2e
## 1: Urban city and town 1758.058 1004.407 117.6261Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 17.213, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7571077 0.8655189
## sample estimates:
## cor
## 0.8184714## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 16.017, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7311467 0.8501570
## sample estimates:
## cor
## 0.7983163## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
How does the correlation look now?
4.2.1.3 Transport
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -5.833, df = 146, p-value = 3.37e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5570107 -0.2940157
## sample estimates:
## cor
## -0.4347367## RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Urban city and town 1848.645 266.0683Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.59071, df = 146, p-value = 0.5556
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1134079 0.2085304
## sample estimates:
## cor
## 0.048829444.2.2 Impute EPC counts
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 211.0 341.8 409.0 466.5 548.2 1140.0## N elec meters## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 430.0 631.5 695.5 733.8 800.8 1392.0Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.3 Estimating the annual emissions levy
Case studies:
- Annual carbon tax
- Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
- September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind
- based on a Marginal Abatement Cost (MAC)
- 2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
4.2.3.1 Scenario 1: Central cost
The table below shows the overall £ GBP total for the case study area in £M.
## £m total## nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: 148 435.3365 44.77884 26.66072## £m by regions covered## region nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: South East 148 435.3365 44.77884 26.66072
The table below shows the mean per dwelling value rounded to the nearest £10.
## beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1: 4200 430 250
## beis_GBPtotal_c_energy_perdw
## 1: 680Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.7: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.8: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1432 2917 3789 4196 5157 11015## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01017249 Southampton 011C Shirley 44958.42 11014.812
## 2: E01017148 Southampton 001D Bassett 43544.19 10668.326
## 3: E01017197 Southampton 020E Freemantle 41429.10 10150.129
## 4: E01017224 Southampton 024C Peartree 31226.09 7650.391
## 5: E01017180 Southampton 002A Coxford 30703.76 7522.422
## 6: E01017214 Southampton 019C Millbrook 30163.70 7390.107## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01017245 Southampton 012E Redbridge 7967.564 1952.053
## 2: E01017241 Southampton 007B Redbridge 7871.967 1928.632
## 3: E01032738 Southampton 022F Bevois 7870.684 1928.318
## 4: E01017182 Southampton 004A Coxford 7344.557 1799.416
## 5: E01017139 Southampton 029A Bargate 7015.385 1718.769
## 6: E01017140 Southampton 023D Bargate 5845.638 1432.181Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.14 332.71 423.12 430.72 530.76 896.55## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01017249 Southampton 011C Shirley 3.659406 896.5545
## 2: E01017148 Southampton 001D Bassett 3.633488 890.2047
## 3: E01017197 Southampton 020E Freemantle 2.998158 734.5488
## 4: E01032753 Southampton 009F Portswood 2.945786 721.7175
## 5: E01017252 Southampton 011D Shirley 2.924247 716.4405
## 6: E01017145 Southampton 001B Bassett 2.903698 711.4061## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01017142 Southampton 029C Bargate 0.6995069 171.379188
## 2: E01032748 Southampton 029G Bargate 0.6532194 160.038752
## 3: E01017140 Southampton 023D Bargate 0.5874720 143.930649
## 4: E01017281 Southampton 032D Woolston 0.3330864 81.606173
## 5: E01032755 Southampton 029I Bargate 0.2409302 59.027907
## 6: E01032746 Southampton 029F Bargate 0.0128181 3.140433Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 160.6 230.0 242.3 246.1 255.8 357.7## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01032746 Southampton 029F Bargate 1.459952 357.6883
## 2: E01017202 Southampton 016C Harefield 1.299459 318.3676
## 3: E01017270 Southampton 003C Swaythling 1.284754 314.7646
## 4: E01017170 Southampton 027D Bitterne 1.265758 310.1107
## 5: E01032748 Southampton 029G Bargate 1.265183 309.9698
## 6: E01017142 Southampton 029C Bargate 1.262154 309.2277## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01017138 Southampton 023C Bargate 0.8657028 212.0972
## 2: E01017160 Southampton 017D Bevois 0.8092219 198.2594
## 3: E01017281 Southampton 032D Woolston 0.7904938 193.6710
## 4: E01017250 Southampton 010B Shirley 0.7889987 193.3047
## 5: E01017196 Southampton 020D Freemantle 0.7812467 191.4054
## 6: E01017278 Southampton 031D Woolston 0.6554957 160.5964Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 275.3 569.1 672.4 676.8 776.5 1194.74.2.3.2 Scenario 2: Rising block tariff
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw## 0% 25% 50% 75% 100%
## 5845.638 11905.745 15464.936 21050.667 44958.416## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw
## 1: 14.056160 1452.5009 526.8518 0.000
## 2: 18.324152 1452.5009 872.0019 1049.332
## 3: 9.203213 1122.7920 0.0000 0.000
## 4: 7.015385 855.8769 0.0000 0.000
## 5: 5.845638 713.1678 0.0000 0.000
## 6: 14.007034 1452.5009 514.8159 0.000
## 7: 26.572009 1452.5009 872.0019 4076.296
## 8: 25.334282 1452.5009 872.0019 3622.050
## 9: 21.013503 1452.5009 872.0019 2036.324
## 10: 25.055866 1452.5009 872.0019 3519.871
## beis_GBPtotal_sc2_perdw
## 1: 1979.3527
## 2: 3373.8348
## 3: 1122.7920
## 4: 855.8769
## 5: 713.1678
## 6: 1967.3167
## 7: 6400.7985
## 8: 5946.5525
## 9: 4360.8268
## 10: 5844.3740| Name | …[] |
| Number of rows | 148 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 17.13 | 7.01 | 5.85 | 11.91 | 15.46 | 21.05 | 44.96 | ▆▇▃▁▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3221.47 | 2302.13 | 713.17 | 1452.80 | 2329.22 | 4374.47 | 13148.61 | ▇▃▂▁▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2184016.07 | 1266099.70 | 650296.60 | 1113364.33 | 1881431.98 | 2831522.94 | 6640047.94 | ▇▅▂▁▁ |
## nLSOAs sum_total_sc1 sum_total_sc2
## 1: 148 435.3365 323.2344
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1: 1900.7450 165.67756
## 2: 2388.2635 165.67756
## 3: 1032.2892 125.93928
## 4: 870.0000 106.14000
## 5: 587.4720 71.67159
## 6: 699.5069 85.33984## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1: 1900.7450 165.67756 90.40898
## 2: 2388.2635 165.67756 90.40898
## 3: 1032.2892 125.93928 0.00000
## 4: 870.0000 106.14000 0.00000
## 5: 587.4720 71.67159 0.00000
## 6: 699.5069 85.33984 0.00000
## beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1: 63.75374 319.84029
## 2: 242.67303 498.75957
## 3: 0.00000 125.93928
## 4: 0.00000 106.14000
## 5: 0.00000 71.67159
## 6: 0.00000 85.33984## [1] 31.10013
## [1] 16.02575
## £m total## nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: 148 323.2344 31.10013 16.02575 252760## £m total by regions covered## region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: South East 148 323.2344 31.10013 16.02575 252760
4.2.4 Estimate retofit costs
- from A-E <- £13,300
- from F-G <- £26,800
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
## To retrofit D-E (£m)## [1] 761.6416## Number of dwellings: 57266## To retrofit F-G (£m)## [1] 146.4769## Number of dwellings: 5466## To retrofit D-G (£m)## [1] 908.1185## To retrofit D-G (mean per dwelling)## [1] 14417.74## meanPerLSOA_GBPm total_GBPm
## 1: 6.135936 908.1185## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.5 Compare levy with costs
4.2.5.1 Scenario 1
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.5.2 Scenario 2
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.6 Years to pay…
4.2.6.1 Scenario 1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.301 2.707 3.745 4.032 4.977 10.307## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.69 18.00 21.43 22.74 25.60 57.64## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Highest retofit sum cost## LSOA11CD LSOA11NM WD18NM retrofitSum yearsToPay epc_D_pc epc_E_pc
## 1: E01017154 Southampton 022B Bevois 14171398 37.90541 0.1505102 0.2806122
## 2: E01017202 Southampton 016C Harefield 11080907 31.66890 0.2780847 0.2412523
## 3: E01017192 Southampton 021A Freemantle 10179160 22.14927 0.4774775 0.1819820
## 4: E01017185 Southampton 002D Coxford 9724814 22.94021 0.4194260 0.2163355
## 5: E01017260 Southampton 026D Sholing 9723096 24.72392 0.4069149 0.2287234
## 6: E01032753 Southampton 009F Portswood 9423102 14.51705 0.4514925 0.2238806
## 7: E01017219 Southampton 028B Peartree 8713800 26.96406 0.3356048 0.1294719
## 8: E01017256 Southampton 026A Sholing 8611899 16.75621 0.5176471 0.1717647
## 9: E01017151 Southampton 006C Bassett 8479424 21.83436 0.4897436 0.2410256
## 10: E01017257 Southampton 026B Sholing 8436594 27.12304 0.4007220 0.1407942
## epc_F_pc epc_G_pc
## 1: 0.191326531 0.122448980
## 2: 0.211786372 0.062615101
## 3: 0.072072072 0.016216216
## 4: 0.136865342 0.017660044
## 5: 0.178191489 0.039893617
## 6: 0.059701493 0.005597015
## 7: 0.097103918 0.013628620
## 8: 0.007058824 0.004705882
## 9: 0.056410256 0.000000000
## 10: 0.102888087 0.009025271
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
4.2.6.2 Scenario 2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.090 3.216 6.080 6.824 9.741 20.699## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.69 18.00 21.43 22.74 25.60 57.64## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
What happens in Year 2 totally depends on the rate of upgrades…
4.2.6.3 Compare scenarios
Comparing pay-back times for the two scenarios - who does the rising block tariff help?
x = y line shown for clarity
5 R environment
5.1 R packages used
- base R (R Core Team 2016)
- bookdown (Xie 2016a)
- data.table (Dowle et al. 2015)
- ggplot2 (Wickham 2009)
- kableExtra (Zhu 2018)
- knitr (Xie 2016b)
- rmarkdown (Allaire et al. 2018)
- skimr (Arino de la Rubia et al. 2017)
5.2 Session info
6 Data Tables
I don’t know if this will work…
## Doesn't